Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method comprising: extracting a set of one or more clinical facts from a text documenting a clinician's encounter with a patient, the extracting comprising analyzing the text to identify a set of one or more features of at least a portion of the text, correlating the set of features to one or more abstract semantic concepts, and generating computer-readable data that expresses the one or more abstract semantic concepts as the one or more clinical facts extracted from the text; analyzing the set of facts, using at least one processor, to identify that at least one fact of the set of facts indicates a potential opportunity for providing additional specificity to the set of facts for documenting the patient encounter; generating one or more hypotheses for an additional fact not documented in the text, that provides the additional specificity indicated by the at least one fact; and alerting a user to at least one of the one or more hypotheses.
The system analyzes clinical notes to identify potential gaps in documentation specificity. It extracts clinical facts from text by identifying text features, mapping them to semantic concepts, and storing these as computer-readable data. It then analyzes these extracted facts to find opportunities for more detail. If it detects such an opportunity, the system generates potential "additional facts" not already present and alerts a user (likely a clinician) to these suggestions. This provides a way to identify if the patient encounter documentation needs further detail.
2. The method of claim 1 , wherein the text comprises a free-form narration of the patient encounter provided by the clinician.
The system described above analyzes clinical notes where the text consists of a free-form, narrative description of the patient encounter provided by the clinician. This means the system is designed to work with unstructured text, rather than relying solely on structured data entry. It helps identify missing specific details even within a doctor's narrative.
3. The method of claim 1 , wherein the analyzing the set of facts comprises analyzing at least one fact received as a discrete structured data item from the user, in addition to the one or more clinical facts extracted from the text.
The system described above, when analyzing the clinical facts, considers not only facts extracted from the clinical text, but also discrete, structured data items manually entered by the user (e.g., a doctor selecting a diagnosis from a dropdown). This allows the system to consider user input alongside information extracted from the text when determining if additional specificity is needed.
4. The method of claim 1 , wherein the analyzing the set of facts comprises analyzing at least one fact received from an electronic medical record of the patient, in addition to the one or more clinical facts extracted from the text.
The system described above, when analyzing the clinical facts, considers not only facts extracted from the clinical text, but also clinical facts retrieved from the patient's existing electronic medical record (EMR). This helps the system determine whether important information is already available in the patient's medical history, which can influence whether or not an alert is generated.
5. The method of claim 1 , wherein the additional fact comprises a more specific version of a first fact of the set of facts.
In the system described above, the generated additional fact is a more specific version of an existing fact extracted from the clinical text. For example, if the system identifies "leg pain," it might suggest the more specific "left lower leg pain."
6. The method of claim 5 , wherein the first fact corresponds to at least one standard code.
In the system described above, the original fact that needs more specificity corresponds to a standard medical code. This means the system can operate on standardized representations of medical concepts.
7. The method of claim 6 , wherein the at least one standard code comprises at least one code selected from the group consisting of an ICD code, a CPT code, a MedDRA code, a SNOMED code, a LOINC code, an RxNorm code, an NDC code and a RadLex code.
In the system described above, the standard medical code can be an ICD code, a CPT code, a MedDRA code, a SNOMED code, a LOINC code, an RxNorm code, an NDC code, or a RadLex code. This is the list of medical coding systems that the original fact can be a part of.
8. The method of claim 5 , wherein the first fact corresponds to a code in a hierarchical coding system, wherein the additional fact corresponds to at least one code in the hierarchical coding system that is a more specific version of the code corresponding to the first fact.
In the system described above, the original fact corresponds to a code within a hierarchical coding system, and the "additional fact" is a more specific code within that same hierarchy. For instance, if a general ICD-10 code is present, the system suggests a more granular ICD-10 code for increased specificity.
9. The method of claim 1 , wherein the additional fact is implied by two or more facts of the set of facts in combination.
In the system described above, the additional fact is not explicitly stated but is logically implied by a combination of two or more facts extracted from the clinical text. The system infers the additional information based on the relationship between multiple observed findings.
10. The method of claim 1 , wherein the generating comprises determining whether the patient's history previous to the patient encounter includes information that provides the additional specificity to the set of facts, and wherein the alerting is performed in response to determining that the patient's history does not include such information.
In the system described above, when generating hypotheses for additional specificity, the system checks the patient's prior medical history. It will only alert the user if the needed additional information is not already present in the patient's history. This avoids redundant alerts.
11. The method of claim 1 , further comprising: in response to determining that the patient's history includes a second additional fact that provides additional specificity to the set of facts, suppressing an alert that would otherwise be generated relating to the second additional fact.
In the system described above, if the patient's history already contains a specific fact that provides the needed additional specificity, the system suppresses the alert that would have otherwise been generated. This avoids unnecessary interruptions for the user if the information is already documented.
12. The method of claim 1 , wherein the alerting comprises displaying a visual alert to the user.
In the system described above, the alert presented to the user is a visual notification.
13. The method of claim 1 , wherein the alerting comprises providing an audio alert to the user.
In the system described above, the alert presented to the user is an audio notification.
14. The method of claim 1 , wherein the alerting comprises presenting one or more options corresponding to the one or more hypotheses, and allowing the user to choose among the one or more options.
In the system described above, the system presents the user with a set of options corresponding to the potential additional facts, allowing the user to select the most appropriate one. This enables interactive refinement of the clinical documentation.
15. The method of claim 14 , further comprising: deriving the additional fact from the option chosen by the user; and storing the additional fact as a discrete structured data item.
In the system described above, after the user selects an option corresponding to an additional fact, the system derives the chosen fact and stores it as a discrete, structured data item. This captures the additional specificity in a readily accessible format.
16. The method of claim 14 , further comprising editing the text in accordance with the option chosen by the user.
In the system described above, after the user selects an option corresponding to an additional fact, the system updates the original clinical text to incorporate the selected fact. This modifies the narrative note to reflect the added detail.
17. The method of claim 1 , wherein the user is the clinician.
In the system described above, the user being alerted is the clinician who is documenting the patient encounter.
18. The method of claim 1 , wherein the user is a person other than the clinician, wherein the alerting comprises prompting the user to provide information corresponding to the additional fact, and wherein the method further comprises prompting the clinician to approve the information provided by the user.
In the system described above, the user being alerted is someone other than the clinician. The system prompts this user to provide the additional information and then prompts the clinician to approve the information provided by the other user. This supports a workflow where non-clinicians assist in documentation.
19. The method of claim 2 , further comprising simultaneously displaying to the user the set of facts and one or more portions of the free-form narration from which the set of facts was extracted.
The system described above, when analyzing free-form clinical notes, displays both the extracted clinical facts and the relevant portions of the original narrative text to the user simultaneously. This provides context and allows the user to understand the basis for the system's suggestions.
20. The method of claim 1 , wherein the alerting is performed before the clinician finally approves the set of one or more clinical facts.
In the system described above, the alerting process occurs before the clinician formally approves the clinical facts. This enables proactive identification of missing specificity before the documentation is finalized.
21. The method of claim 1 , wherein the analyzing the set of facts, the generating, and the alerting are performed automatically.
In the system described above, the analysis of facts, the generation of hypotheses, and the alerting of the user are all performed automatically by the system.
22. The method of claim 17 , further comprising: receiving an indication from a human other than the clinician to issue an additional alert to the clinician; and in response to the indication, issuing the additional alert to the clinician.
In the system described above, a person besides the clinician can trigger an additional alert for the clinician, and the system will issue that alert. This enables a secondary review and prompting workflow.
23. Apparatus comprising: at least one processor; and a memory storing processor-executable instructions that, when executed by the at least one processor, perform a method comprising: extracting a set of one or more clinical facts from a text documenting a clinician's encounter with a patient, the extracting comprising analyzing the text to identify a set of one or more features of at least a portion of the text, correlating the set of features to one or more abstract semantic concepts, and generating computer-readable data that expresses the one or more abstract semantic concepts as the one or more clinical facts extracted from the text; analyzing the set of facts to identify that at least one fact of the set of facts indicates a potential opportunity for providing additional specificity to the set of facts for documenting the patient encounter; generating one or more hypotheses for an additional fact not documented in the text, that provides the additional specificity indicated by the at least one fact; and alerting a user to at least one of the one or more hypotheses.
The system analyzes clinical notes using a processor and memory to identify potential gaps in documentation specificity. It extracts clinical facts from text by identifying text features, mapping them to semantic concepts, and storing these as computer-readable data. It then analyzes these extracted facts to find opportunities for more detail. If it detects such an opportunity, the system generates potential "additional facts" not already present and alerts a user (likely a clinician) to these suggestions. This provides a way to identify if the patient encounter documentation needs further detail.
24. The apparatus of claim 23 , wherein the text comprises a free-form narration of the patient encounter provided by the clinician.
The system described above analyzes clinical notes where the text consists of a free-form, narrative description of the patient encounter provided by the clinician. This means the system is designed to work with unstructured text, rather than relying solely on structured data entry. It helps identify missing specific details even within a doctor's narrative.
25. The apparatus of claim 23 , wherein the analyzing the set of facts comprises analyzing at least one fact received as a discrete structured data item from the user, in addition to the one or more clinical facts extracted from the text.
The system described above, when analyzing the clinical facts, considers not only facts extracted from the clinical text, but also discrete, structured data items manually entered by the user (e.g., a doctor selecting a diagnosis from a dropdown). This allows the system to consider user input alongside information extracted from the text when determining if additional specificity is needed.
26. The apparatus of claim 23 , wherein the analyzing the set of facts comprises analyzing at least one fact received from an electronic medical record of the patient, in addition to the one or more clinical facts extracted from the text.
The system described above, when analyzing the clinical facts, considers not only facts extracted from the clinical text, but also clinical facts retrieved from the patient's existing electronic medical record (EMR). This helps the system determine whether important information is already available in the patient's medical history, which can influence whether or not an alert is generated.
27. The apparatus of claim 23 , wherein the additional fact comprises a more specific version of a first fact of the set of facts.
In the system described above, the generated additional fact is a more specific version of an existing fact extracted from the clinical text. For example, if the system identifies "leg pain," it might suggest the more specific "left lower leg pain."
28. The apparatus of claim 27 , wherein the first fact corresponds to at least one standard code.
In the system described above, the original fact that needs more specificity corresponds to a standard medical code. This means the system can operate on standardized representations of medical concepts.
29. The apparatus of claim 28 , wherein the at least one standard code comprises at least one code selected from the group consisting of an ICD code, a CPT code, a MedDRA code, a SNOMED code, a LOINC code, an RxNorm code, an NDC code and a RadLex code.
In the system described above, the standard medical code can be an ICD code, a CPT code, a MedDRA code, a SNOMED code, a LOINC code, an RxNorm code, an NDC code, or a RadLex code. This is the list of medical coding systems that the original fact can be a part of.
30. The apparatus of claim 27 , wherein the first fact corresponds to a code in a hierarchical coding system, wherein the additional fact corresponds to at least one code in the hierarchical coding system that is a more specific version of the code corresponding to the first fact.
In the system described above, the original fact corresponds to a code within a hierarchical coding system, and the "additional fact" is a more specific code within that same hierarchy. For instance, if a general ICD-10 code is present, the system suggests a more granular ICD-10 code for increased specificity.
31. The apparatus of claim 23 , wherein the additional fact is implied by two or more facts of the set of facts in combination.
In the system described above, the additional fact is not explicitly stated but is logically implied by a combination of two or more facts extracted from the clinical text. The system infers the additional information based on the relationship between multiple observed findings.
32. The apparatus of claim 23 , wherein the generating comprises determining whether the patient's history previous to the patient encounter includes information that provides the additional specificity to the set of facts, and wherein the alerting is performed in response to determining that the patient's history does not include such information.
In the system described above, when generating hypotheses for additional specificity, the system checks the patient's prior medical history. It will only alert the user if the needed additional information is not already present in the patient's history. This avoids redundant alerts.
33. The apparatus of claim 23 , wherein the method further comprises: in response to determining that the patient's history includes a second additional fact that provides additional specificity to the set of facts, suppressing an alert that would otherwise be generated relating to the second additional fact.
In the system described above, if the patient's history already contains a specific fact that provides the needed additional specificity, the system suppresses the alert that would have otherwise been generated. This avoids unnecessary interruptions for the user if the information is already documented.
34. The apparatus of claim 23 , wherein the alerting comprises displaying a visual alert to the user.
In the system described above, the alert presented to the user is a visual notification.
35. The apparatus of claim 23 , wherein the alerting comprises providing an audio alert to the user.
In the system described above, the alert presented to the user is an audio notification.
36. The apparatus of claim 23 , wherein the alerting comprises presenting one or more options corresponding to the one or more hypotheses, and allowing the user to choose among the one or more options.
In the system described above, the system presents the user with a set of options corresponding to the potential additional facts, allowing the user to select the most appropriate one. This enables interactive refinement of the clinical documentation.
37. The apparatus of claim 36 , wherein the method further comprises: deriving the additional fact from the option chosen by the user; and storing the additional fact as a discrete structured data item.
In the system described above, after the user selects an option corresponding to an additional fact, the system derives the chosen fact and stores it as a discrete, structured data item. This captures the additional specificity in a readily accessible format.
38. The apparatus of claim 36 , wherein the method further comprises editing the text in accordance with the option chosen by the user.
In the system described above, after the user selects an option corresponding to an additional fact, the system updates the original clinical text to incorporate the selected fact. This modifies the narrative note to reflect the added detail.
39. The apparatus of claim 23 , wherein the user is the clinician.
In the system described above, the user being alerted is the clinician who is documenting the patient encounter.
40. The apparatus of claim 23 , wherein the user is a person other than the clinician, wherein the alerting comprises prompting the user to provide information corresponding to the additional fact, and wherein the method further comprises prompting the clinician to approve the information provided by the user.
In the system described above, the user being alerted is someone other than the clinician. The system prompts this user to provide the additional information and then prompts the clinician to approve the information provided by the other user. This supports a workflow where non-clinicians assist in documentation.
41. The apparatus of claim 24 , wherein the method further comprises simultaneously displaying to the user the set of facts and one or more portions of the free-form narration from which the set of facts was extracted.
The system described above, when analyzing free-form clinical notes, displays both the extracted clinical facts and the relevant portions of the original narrative text to the user simultaneously. This provides context and allows the user to understand the basis for the system's suggestions.
42. The apparatus of claim 23 , wherein the alerting is performed before the clinician finally approves the set of one or more clinical facts.
In the system described above, the alerting process occurs before the clinician formally approves the clinical facts. This enables proactive identification of missing specificity before the documentation is finalized.
43. The apparatus of claim 23 , wherein the analyzing the set of facts, the generating, and the alerting are performed automatically.
In the system described above, the analysis of facts, the generation of hypotheses, and the alerting of the user are all performed automatically by the system.
44. The apparatus of claim 39 , wherein the method further comprises: receiving an indication from a human other than the clinician to issue an additional alert to the clinician; and in response to the indication, issuing the additional alert to the clinician.
In the system described above, a person besides the clinician can trigger an additional alert for the clinician, and the system will issue that alert. This enables a secondary review and prompting workflow.
45. At least one non-transitory computer-readable storage medium encoded with a plurality of computer-executable instructions that, when executed, perform a method comprising: extracting a set of one or more clinical facts from a text documenting a clinician's encounter with a patient, the extracting comprising analyzing the text to identify a set of one or more features of at least a portion of the text, correlating the set of features to one or more abstract semantic concepts, and generating computer-readable data that expresses the one or more abstract semantic concepts as the one or more clinical facts extracted from the text; analyzing the set of facts to identify that at least one fact of the set of facts indicates a potential opportunity for providing additional specificity to the set of facts for documenting the patient encounter; generating one or more hypotheses for an additional fact not documented in the text, that provides the additional specificity indicated by the at least one fact; and alerting a user to at least one of the one or more hypotheses.
The system, stored on a non-transitory computer-readable medium, analyzes clinical notes to identify potential gaps in documentation specificity. It extracts clinical facts from text by identifying text features, mapping them to semantic concepts, and storing these as computer-readable data. It then analyzes these extracted facts to find opportunities for more detail. If it detects such an opportunity, the system generates potential "additional facts" not already present and alerts a user (likely a clinician) to these suggestions. This provides a way to identify if the patient encounter documentation needs further detail.
46. The at least one non-transitory computer-readable storage medium of claim 45 , wherein the text comprises a free-form narration of the patient encounter provided by the clinician.
The system described above analyzes clinical notes where the text consists of a free-form, narrative description of the patient encounter provided by the clinician. This means the system is designed to work with unstructured text, rather than relying solely on structured data entry. It helps identify missing specific details even within a doctor's narrative.
47. The at least one non-transitory computer-readable storage medium of claim 45 , wherein the analyzing the set of facts comprises analyzing at least one fact received as a discrete structured data item from the user, in addition to the one or more clinical facts extracted from the text.
The system described above, when analyzing the clinical facts, considers not only facts extracted from the clinical text, but also discrete, structured data items manually entered by the user (e.g., a doctor selecting a diagnosis from a dropdown). This allows the system to consider user input alongside information extracted from the text when determining if additional specificity is needed.
48. The at least one non-transitory computer-readable storage medium of claim 45 , wherein the analyzing the set of facts comprises analyzing at least one fact received from an electronic medical record of the patient, in addition to the one or more clinical facts extracted from the text.
The system described above, when analyzing the clinical facts, considers not only facts extracted from the clinical text, but also clinical facts retrieved from the patient's existing electronic medical record (EMR). This helps the system determine whether important information is already available in the patient's medical history, which can influence whether or not an alert is generated.
49. The at least one non-transitory computer-readable storage medium of claim 45 , wherein the additional fact comprises a more specific version of a first fact of the set of facts.
In the system described above, the generated additional fact is a more specific version of an existing fact extracted from the clinical text. For example, if the system identifies "leg pain," it might suggest the more specific "left lower leg pain."
50. The at least one non-transitory computer-readable storage medium of claim 49 , wherein the first fact corresponds to at least one standard code.
In the system described above, the original fact that needs more specificity corresponds to a standard medical code. This means the system can operate on standardized representations of medical concepts.
51. The at least one non-transitory computer-readable storage medium of claim 50 , wherein the at least one standard code comprises at least one code selected from the group consisting of an ICD code, a CPT code, a MedDRA code, a SNOMED code, a LOINC code, an RxNorm code, an NDC code and a RadLex code.
In the system described above, the standard medical code can be an ICD code, a CPT code, a MedDRA code, a SNOMED code, a LOINC code, an RxNorm code, an NDC code, or a RadLex code. This is the list of medical coding systems that the original fact can be a part of.
52. The at least one non-transitory computer-readable storage medium of claim 49 , wherein the first fact corresponds to a code in a hierarchical coding system, wherein the additional fact corresponds to at least one code in the hierarchical coding system that is a more specific version of the code corresponding to the first fact.
In the system described above, the original fact corresponds to a code within a hierarchical coding system, and the "additional fact" is a more specific code within that same hierarchy. For instance, if a general ICD-10 code is present, the system suggests a more granular ICD-10 code for increased specificity.
53. The at least one non-transitory computer-readable storage medium of claim 45 , wherein the additional fact is implied by two or more facts of the set of facts in combination.
In the system described above, the additional fact is not explicitly stated but is logically implied by a combination of two or more facts extracted from the clinical text. The system infers the additional information based on the relationship between multiple observed findings.
54. The at least one non-transitory computer-readable storage medium of claim 45 , wherein the generating comprises determining whether the patient's history previous to the patient encounter includes information that provides the additional specificity to the set of facts, and wherein the alerting is performed in response to determining that the patient's history does not include such information.
In the system described above, when generating hypotheses for additional specificity, the system checks the patient's prior medical history. It will only alert the user if the needed additional information is not already present in the patient's history. This avoids redundant alerts.
55. The at least one non-transitory computer-readable storage medium of claim 45 , wherein the method further comprises: in response to determining that the patient's history includes a second additional fact that provides additional specificity to the set of facts, suppressing an alert that would otherwise be generated relating to the second additional fact.
In the system described above, if the patient's history already contains a specific fact that provides the needed additional specificity, the system suppresses the alert that would have otherwise been generated. This avoids unnecessary interruptions for the user if the information is already documented.
56. The at least one non-transitory computer-readable storage medium of claim 45 , wherein the alerting comprises displaying a visual alert to the user.
In the system described above, the alert presented to the user is a visual notification.
57. The at least one non-transitory computer-readable storage medium of claim 45 , wherein the alerting comprises providing an audio alert to the user.
In the system described above, the alert presented to the user is an audio notification.
58. The at least one non-transitory computer-readable storage medium of claim 45 , wherein the alerting comprises presenting one or more options corresponding to the one or more hypotheses, and allowing the user to choose among the one or more options.
In the system described above, the system presents the user with a set of options corresponding to the potential additional facts, allowing the user to select the most appropriate one. This enables interactive refinement of the clinical documentation.
59. The at least one non-transitory computer-readable storage medium of claim 58 , wherein the method further comprises: deriving the additional fact from the option chosen by the user; and storing the additional fact as a discrete structured data item.
In the system described above, after the user selects an option corresponding to an additional fact, the system derives the chosen fact and stores it as a discrete, structured data item. This captures the additional specificity in a readily accessible format.
60. The at least one non-transitory computer-readable storage medium of claim 58 , wherein the method further comprises editing the text in accordance with the option chosen by the user.
In the system described above, after the user selects an option corresponding to an additional fact, the system updates the original clinical text to incorporate the selected fact. This modifies the narrative note to reflect the added detail.
61. The at least one non-transitory computer-readable storage medium of claim 45 , wherein the user is the clinician.
In the system described above, the user being alerted is the clinician who is documenting the patient encounter.
62. The at least one non-transitory computer-readable storage medium of claim 45 , wherein the user is a person other than the clinician, wherein the alerting comprises prompting the user to provide information corresponding to the additional fact, and wherein the method further comprises prompting the clinician to approve the information provided by the user.
In the system described above, the user being alerted is someone other than the clinician. The system prompts this user to provide the additional information and then prompts the clinician to approve the information provided by the other user. This supports a workflow where non-clinicians assist in documentation.
63. The at least one non-transitory computer-readable storage medium of claim 46 , wherein the method further comprises simultaneously displaying to the user the set of facts and one or more portions of the free-form narration from which the set of facts was extracted.
The system described above, when analyzing free-form clinical notes, displays both the extracted clinical facts and the relevant portions of the original narrative text to the user simultaneously. This provides context and allows the user to understand the basis for the system's suggestions.
64. The at least one non-transitory computer-readable storage medium of claim 45 , wherein the alerting is performed before the clinician finally approves the set of one or more clinical facts.
In the system described above, the alerting process occurs before the clinician formally approves the clinical facts. This enables proactive identification of missing specificity before the documentation is finalized.
65. The at least one non-transitory computer-readable storage medium of claim 45 , wherein the analyzing the set of facts, the generating, and the alerting are performed automatically.
In the system described above, the analysis of facts, the generation of hypotheses, and the alerting of the user are all performed automatically by the system.
66. The at least one non-transitory computer-readable storage medium of claim 61 , wherein the method further comprises: receiving an indication from a human other than the clinician to issue an additional alert to the clinician; and in response to the indication, issuing the additional alert to the clinician.
In the system described above, a person besides the clinician can trigger an additional alert for the clinician, and the system will issue that alert. This enables a secondary review and prompting workflow.
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August 5, 2014
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